Although banks and other financial institutions have gained control of credit card fraud over the last decade using tools such as the Fair Isaac Falcon fraud scoring system, fraudsters continue to evolve, and they see credit and debit cards as rich targets. Among other approaches, financial institutions use various behavior monitoring tracking techniques to identify out-of-the-ordinary behavior as potentially fraudulent. For example, where a particular customer typically does not purchase goods of a particular type, and then makes multiple purchases of such goods, the tracking system may raise a red flag, as it is possible that the customer's card has been stolen. Also, certain types of goods such as jewelry and electronics, and other goods that are easily transferable into cash, may also tend to signal a fraudulent transaction. Multiple purchases in a short time period may do the same, as may purchases far from a customer's home.
Factors like these may be combined into a fraud score, which is typically a numeric value or values that represent a weighting of the various factors that suggest that a transaction is or is not fraudulent. That fraud score may then be used by a financial institution to determine whether to take action. Typically, such action involves a “referral,” which often produces a rejection of a transaction and follow-up by the financial institution's customer service department. For example, a customer may be contacted telephonically by a customer service representative at a telephone number associated with the account, to confirm whether the customer is aware of the transaction. Other follow-up measures may also be taken.
There are costs associated with “false negatives,” i.e., failures to identify real fraud, and “false positives,” i.e., referrals when no fraud has occurred. The costs of false negatives are fairly obvious: the financial institution may have to cover the cost of some or all of the transaction(s) for the customer. The costs of false positives include the cost of the customer service operation and the cost of dissatisfied customers who may have to spend time clearing up the confusion and also may have their transaction(s) denied. When multiplied across millions or billions of transactions, such costs can be enormous. Thus, a fraud detection system or method should generally identify attempted fraud when it does occur, and not identify activity as fraud when it is legitimate activity.
The simplest constraint in determining what transactions to refer is referral volume. Specifically, a corporation's ability to process referrals for fraud may serve as a limit on the number of referrals that a fraud detection system may make. Thus, systems may permit flexibility in setting the standard for which case to refer. In one form, an “optimized” form of a Falcon score (converting the score to the probability of fraud), which estimates the expected net savings of a referral, has been used. Likewise, a set of optimal rules has shifted the inquiry from a score-based approach to a more standardized approach, in attempting to optimize value given a particular volume constraint. Such rules provide easier interpretation by users of the system, which may lead to greater acceptance of a fraud detection technique. Such rule optimization has also been extended to include multiple constraints and an assessment of potential attrition effects of false positives. Yet another system uses a referral score that includes discount factors of expected recovery rates for different fraud transaction types. Such techniques are generally complementary to the Falcon score approach, and increase its effectiveness.
For each of these approaches, the analysis, and hence the optimization, was performed at the transaction level. Transaction-level formulations can be solved using established optimization techniques. Referrals are transaction-level actions, but their impact is felt by the consumer, and so fraud strategies are typically implemented at the account level. In contrast, the account-level problem involves non-linear considerations, and is thus more complicated.